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癫痫的个体化虚拟大脑模型。

Personalised virtual brain models in epilepsy.

机构信息

Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France.

Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France.

出版信息

Lancet Neurol. 2023 May;22(5):443-454. doi: 10.1016/S1474-4422(23)00008-X. Epub 2023 Mar 24.

Abstract

Individuals with drug-resistant focal epilepsy are candidates for surgical treatment as a curative option. Before surgery can take place, the patient must have a presurgical evaluation to establish whether and how surgical treatment might stop their seizures without causing neurological deficits. Virtual brains are a new digital modelling technology that map the brain network of a person with epilepsy, using data derived from MRI. This technique produces a computer simulation of seizures and brain imaging signals, such as those that would be recorded with intracranial EEG. When combined with machine learning, virtual brains can be used to estimate the extent and organisation of the epileptogenic zone (ie, the brain regions related to seizure generation and the spatiotemporal dynamics during seizure onset). Virtual brains could, in the future, be used for clinical decision making, to improve precision in localisation of seizure activity, and for surgical planning, but at the moment these models have some limitations, such as low spatial resolution. As evidence accumulates in support of the predictive power of personalised virtual brain models, and as methods are tested in clinical trials, virtual brains might inform clinical practice in the near future.

摘要

患有耐药性局灶性癫痫的个体是手术治疗的候选者,作为一种治愈选择。在进行手术之前,患者必须进行术前评估,以确定手术治疗是否以及如何在不引起神经功能缺损的情况下停止癫痫发作。虚拟大脑是一种新的数字建模技术,它使用源自 MRI 的数据来绘制癫痫患者的大脑网络。该技术产生了癫痫发作和大脑成像信号的计算机模拟,例如可以通过颅内 EEG 记录的信号。当与机器学习结合使用时,虚拟大脑可用于估计致痫区(即与癫痫发作相关的大脑区域以及癫痫发作开始时的时空动力学)的范围和组织。虚拟大脑将来可用于临床决策,以提高癫痫活动定位的精度,并用于手术计划,但目前这些模型存在一些局限性,例如空间分辨率低。随着支持个性化虚拟大脑模型预测能力的证据不断积累,并且随着方法在临床试验中进行测试,虚拟大脑可能会在不久的将来为临床实践提供信息。

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